Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Region Normalization for Image Inpainting
Authors: Tao Yu, Zongyu Guo, Xin Jin, Shilin Wu, Zhibo Chen, Weiping Li, Zhizheng Zhang, Sen Liu12733-12740
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that our method outperforms current state-of-the-art methods quantitatively and qualitatively. |
| Researcher Affiliation | Academia | CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System, University of Science and Technology of China EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or explicitly labeled algorithm blocks. |
| Open Source Code | Yes | 1The codes are available at https://github.com/geekyutao/RN |
| Open Datasets | Yes | We evaluate our methods on Places2 (Zhou et al. 2017) and Celeb A (Liu et al. 2015) datasets. |
| Dataset Splits | No | The paper mentions testing on 'total validation data (36500 images) of Places2' but does not provide explicit train/validation/test dataset splits (e.g., percentages or counts) for the datasets used. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU models, CPU types, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | We set threshold t = 0.8 in this work. We apply RN-B in the early layers (encoder) of our generator and RN-L in the intermediate and later layers (the residual blocks and decoder). |